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GEO
Generative Engine Optimization, built for what actually gets cited, not just clean markup.
Schema and an llms.txt make your work legible to a model. Substance is what makes it worth quoting. Most GEO stops at the first; we build both.
On this page
GEO is usually sold as a markup job. Add structured data, clean up the semantic HTML, drop in an llms.txt. That work matters and it is the easy part. It makes your pages legible to a model. It does not decide whether the model picks you.
What decides that is whether the page says something worth repeating. People increasingly ask a model[1] instead of scrolling a results page, and a growing share of those answers resolve without a click at all.[2] The model builds its answer by pulling from sources it can both parse and trust, and the ones it cites tend to have a real answer in them: a number only they have, a genuine comparison, proof you can check.
So we build GEO in two layers. The technical layer, so a model can read you. The substance layer, so it has a reason to.
Clean markup makes your work legible to a model. It does not make it worth citing. Legibility is the floor, not the result.
The technical layer is table stakes
JSON-LD structured data, clean semantic HTML, an llms.txt that lays the work out for crawlers, and pages fast enough that a crawler actually fetches them. This site is built exactly that way, and we ship it for clients.
It is necessary, and it is finite. Once a model can read you cleanly, more markup does not earn more citations. This is the floor. Most GEO offers stop here and call it the whole job, which is why so much of it is low effort and low return at the same time.
What actually earns the citation
The work that compounds sits on the page itself, in what it says. These are the moves we build and run, and none of them is markup.
- Original data. A figure or finding only you have is the most citable thing you can publish, because a model cannot get it anywhere else. We help you produce it, then structure it so the model can lift it cleanly.
- Honest comparison content. A lot of what people ask a model is comparative: which option, for whom, at what cost. A clear, fair comparison answers the exact question and tends to get pulled whole.
- Proof with numbers. Specific, verifiable outcomes beat adjectives. A concrete result a model can quote does what 'trusted by leaders' never will.
- Claims written to be quoted. One idea per block, the answer near the top, self-contained enough that a model can repeat it without rewriting it.
Why substance moves the needle and schema does not
The technical work is quick and its returns flatten fast. A model does not reward you for more tags; it rewards a source it can trust and reuse. Trust is built on what you actually say, not how you mark it up.
And the stakes moved. As AI answers resolve more questions without sending a click,[2] being the source the answer is built from matters more than ranking a link nobody presses. You do not win that by being readable. You win it by being worth quoting.
How we run it
We start with what a model can currently read and cite about you, and where the gaps are. We fix the technical layer once, then put the effort where it pays: the substance that earns a citation. It runs alongside your other channels, and you keep your content, your data, and your site.
Sources
- 1Pew Research Center, Americans and AI 2026: chatbots, smart devices, and views on impact (2026)
- 2Pew Research Center, Google users are less likely to click on links when an AI summary appears (2025)